Interested in this Data Scientist role at Radwell International?
Apply Now →Skills & Technologies
About This Role
Who We Are:
At Radwell, we’ve been shaping a brighter future for industry for over 45 years. As a global leader in Automation, MRO (Maintenance, Repair, and Operations) and Electrical solutions, we’re committed to providing top\-notch services to our customers. We help customers maximize their uptime and performance, supplying new and pre\-owned parts, including obsolete and hard\-to\-find items. Additionally, our sophisticated repair technicians can repair customers’ parts and get those back into production quickly. Radwell is a global business with locations throughout the United States, Canada, Mexico, and Europe. Come challenge yourself and be a part of our rapid growth story.
The position is full\-time and will be based at our Chicago\-area location in Downers Grove, IL.
Job Overview:
We are seeking a highly analytical and results\-driven Senior Pricing Analyst to join our team. In this role, you will be responsible for leveraging data science, advanced analytics, and machine learning techniques to develop and optimize pricing strategies that maximize profitability, improve market competitiveness, and align with business objectives. The ideal candidate will have a strong background in statistical modeling, economics, and pricing optimization, and be able to communicate complex findings to non\-technical stakeholders.
Key Responsibilities:
- Pricing Strategy and Optimization: Design and implement data\-driven pricing strategies that align with the company's revenue and profit goals, taking into account market conditions, customer behavior, and competitor pricing. Build in\-house price optimization engine applying machine learning and optimization techniques to determine optimal pricing and discounting strategies for products and services.
- Data Analysis \& Insights: Analyze large (sometimes messy and incomplete) datasets, including sales, customer, and market data, to uncover insights and trends that can inform pricing decisions. Build and maintain pricing models to forecast demand elasticity, optimize price points, and identify opportunities for margin improvement.
- Competitive Intelligence: Monitor competitor pricing, market trends, and industry benchmarks to inform pricing models and strategy. Provide recommendations based on competitive analysis and customer segmentation.
- Collaboration: Work closely with cross\-functional teams such as Marketing, Product, and Finance to ensure pricing strategies are aligned with overall business objectives and market positioning.
- Reporting \& Presentation: Prepare and present pricing analysis, insights, and recommendations to senior management and key stakeholders, ensuring that data\-driven decisions are easily understood and actionable.
- Continuous Improvement: Stay current with developments in data science, machine learning, and pricing methodologies. Continuously assess the performance of pricing strategies and iterate based on feedback and market conditions.
Qualifications:
- Education:
- Bachelor's degree in Economics, Statistics, Mathematics, Data Science, Business, or a related field (Master's or Ph.D. is a plus).
- Experience:
- 3\+ years of experience in analytics, pricing science, revenue management, or a similar analytical role.
- Proven experience with pricing models, pricing optimization techniques, and demand forecasting.
- Familiarity with machine learning, regression models, and statistical analysis in the context of pricing.
Skills \& Competencies:
- Strong proficiency in data analysis tools and languages (e.g., SQL, Python, R, Excel, etc.).
- Solid understanding of pricing strategies, elasticity, segmentation, and competitive analysis.
- Ability to interpret complex data and translate it into actionable pricing recommendations.
- Strong problem\-solving skills and a creative, data\-driven mindset.
- Excellent communication and presentation skills, with the ability to explain technical findings to non\-technical stakeholders.
SALARY RANGE INFORMATION:
- The range displayed on each job posting reflects the targeted base salary for the position. Within the range, individual pay is determined by work location and additional factors, including job related\-skills, experience, and relevant education or training.
SALARY RANGE:
Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Radwell International, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $198,000 based on 808 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Radwell International AI Hiring
Radwell International has 1 open AI role right now. They're hiring across Data Scientist. Based in Downers Grove, IL, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.